Intelligent Control Systems: Are They for Real?

1993 ◽  
Vol 115 (2B) ◽  
pp. 392-401 ◽  
Author(s):  
Rahmat Shoureshi
Keyword(s):  
Neural Networks ◽  
Optimal Control ◽  
Control Systems ◽  
Intelligent Control ◽  
Dynamic Systems ◽  
Specific Problem ◽  
Human Comfort ◽  
Intelligent Control Systems ◽  
Control Dynamic ◽  
New Challenges

The fundamental concept of feedback to control dynamic systems has played a major role in many areas of engineering. Increases in complexity and more stringent requirements have introduced new challenges for control systems. This paper presents an introduction to and appreciation for intelligent control systems, their application areas, and justifies their need. Specific problem related to automated human comfort control is discussed. Some analytical derivations related to neural networks and fuzzy optimal control as elements of proposed intelligent control systems, along with experimental results, are presented. A brief glossary of common terminology used in this area is included.

scholarly journals NEURAL NETWORK INTERPOLATION PARAMETERS OF A MULTI-MODE DYNAMIC MODEL OF THE AIRCRAFT ENGINE

2020 ◽  
pp. 98-104
Author(s):  
Aleksandr Tamargazin ◽  
Liudmyla Pryimak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control Systems ◽  
Intelligent Control ◽  
Aircraft Engine ◽  
Aircraft Engines ◽  
Development Trends ◽  
Suggested Approach ◽  
Intelligent Control Systems ◽  
The Neural Network

The foundations of the concept of creation of intelligent aircraft engine control systems based on the decomposition of control processes within the architecture of open information systems are considered. Unlike well-known approaches, the suggested approach allows achieving the management goal based on the principle of minimum entropy by redistributing system resources in conditions of their shortage, as well as adapting system characteristics when changing the management situation based on self-learning and self-organization of intelligent control systems. Based on an analysis of the development trends of aircraft engines, as well as development trends of production and technological systems, including the creation of new composite materials and new technologies for the manufacture and control of parts and components of aircraft engines, the intellectualization of their automatic control systems is discussed. Moreover, the development trends of aircraft engine control systems are considered from the development of their structures, functions, properties, and abilities for new qualitative changes. The article gives the general characteristics and the main directions of the design of intelligent control systems for aircraft engines as complex technical objects. The problem of designing nonlinear dynamic models of aircraft engines using artificial neural networks is discussed. The statement of this problem and possible approaches to its solution are being formed. The results of the neural network identification of an aircraft engine are compared using the least-squares method. Such a technique for designing a model of aircraft engines makes it possible to indirectly calculate engine coordinates inaccessible to measurement - traction, fuel consumption, etc. The suggested approach allows calculation of the design of neural networks simulating aircraft engines at each step using standard procedures, which makes it possible to automate the creation of neural networks. To reduce the computation time, it is suggested using the optimization algorithms taking into account changes in the state entropy. This simplifies the implementation of the neural network model of an aircraft engine in real time as part of an onboard computer complex.

MODELING OF NEURAL PREDICTIVE CONTROL OF IRRIGATION MACHINES

2021 ◽  
pp. 14-22
Author(s):  
G. N. KAMYSHOVA ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Predictive Control ◽  
Intelligent Control ◽  
Machine Learning Algorithms ◽  
Intelligent Control Systems ◽  
Artificial Neural ◽  
The One ◽  
Neural Predictive Control

The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.

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